Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Aznatkoiny/zAI-Skills --skill prompt-optimizergit clone --depth 1 https://github.com/Aznatkoiny/zAI-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/aznatkoiny/zai-skills/prompt-optimizer)<a href="https://agentmods.dev/skills/aznatkoiny/zai-skills/prompt-optimizer"><img src="https://agentmods.dev/badge/skills/aznatkoiny/zai-skills/prompt-optimizer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/aznatkoiny/zai-skills/prompt-optimizer"><img src="https://agentmods.dev/badge/skills/aznatkoiny/zai-skills/prompt-optimizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00205 | $0.02005 |
| Opus 5 | $0.00102 | $0.01002 |
| Sonnet 5 | $0.00041 | $0.00401 |
| Haiku 4.5 | $0.00020 | $0.00200 |
Grade A, and why
prompt-optimizer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Optimizer
Optimize prompts for Claude's latest models — the Claude 5 family (Opus 5, Sonnet 5, Fable 5) plus the still-active Opus 4.8, Sonnet 4.6, and Haiku 4.5 — using Anthropic's official guidance. Diagnose what is wrong, pick the right lever, apply a proven fix, then measure.
Two levers, not one
Many problems people try to fix by editing prompt wording are better fixed with an API parameter. Always weigh both:
- Prompt — wording, structure, examples, role, explicit scope.
- Parameters —
effort(intelligence vs. speed/cost),thinking(adaptive on/off),max_tokens(room to think and act).
On the latest models, effort is frequently the highest-leverage change. If reasoning is shallow on a hard task, raise effort to high/xhigh instead of prompting around it. If the model overthinks or over-explores, lower it. Detail in references/model-tuning.md.
What changed for the latest models
These shifts change how to optimize. Do not carry over old habits blindly:
- Effort governs depth, tool use, and thoroughness. Start
xhighfor coding/agentic work, minimumhighfor intelligence-sensitive work. Lower effort means more literal, more scoped, less tool use. - Thinking defaults changed with Claude 5. On the Claude 5 family thinking is on by default (Fable 5 rejects an explicit
disabled); on Opus 4.8 it stays off unless you setthinking: {type: "adaptive"}. Opus 4.6 and Sonnet 4.6 use adaptive thinking.budget_tokensis removed on Claude 5 / Opus 4.8 — control depth througheffort. - Literal instruction following. The model does what you said, not what you implied. State scope explicitly ("apply to every section, not just the first one").
- Dial back anti-laziness prompting. Aggressive "CRITICAL / You MUST" language needed for older models now causes overtriggering. Use normal phrasing ("Use this tool when...").
- Prefilled assistant responses are no longer supported on 4.6+ models (the request returns a 400). Migrations in references/formatting.md.
- Verbosity is calibrated to task complexity and prose is more direct. Tune with positive examples of the concision you want, not "don't" rules.
- Subagent appetite differs by model. Opus 4.8 under-delegates; Claude Opus 5 delegates readily. Steer explicitly — and cap fan-out on Opus 5.
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 90 lines · 205 tokens per session scan A aa1743e22f51
prompt-optimizer is a skill published in the GitHub repository Aznatkoiny/zAI-Skills (9 stars, last pushed 1mo ago), licensed MIT. It adds 205 tokens to every session and 2,005 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
text-optimizer
Optimizes text, prompts, and documentation for LLM token efficiency. Applies 52 research-backed rules across 8 categories: Claude behavior, token efficiency, structure, deduplication, reference integrity, perception, LLM comprehension, and aggressive lossy (deep only). Use when optimizing prompts, reducing tokens…
meta-prompting
Enhanced reasoning patterns via slash commands (/think, /verify, /adversarial, /edge, /compare, /confidence, /budget, /constrain, /json, /flip, /assumptions, /tensions, /analyze, /trade) or natural language ("argue against", "what could break", "show reasoning", "deep review", "meta-prompts", "thinking modes"…
refine-prompt
Transforms vague or rough prompts into precise, structured AI instructions. Use when asked to "refine prompt", "improve prompt", "make this prompt better", "promptify", "optimize prompt", "rewrite prompt", "enhance prompt", or "sharpen instructions".
ccc-prompt-fix
Fix and sharpen a prompt. Diagnoses it against the 6 prompt-quality patterns, returns a tightened rewrite with the reasoning, and suggests the right library prompt for your task.
gws-modelarmor-create-template
Google Model Armor: Create a new Model Armor template.
gws-modelarmor-sanitize-prompt
Google Model Armor: Sanitize a user prompt through a Model Armor template.